
In FLchain, attacks can make attempt to train a local model using designed falsified data to replace the global model and modify parameter values during model transmission, aiming to manipulate the training output. blackMost of existing works focus on building attack detection mechanisms for model training and update transmission in FLchain [1]}, [2]}, [3]}.

The possibility of surface attacks (e.g., poisoning ones on training data) can be reduced by adjusting mining difficulty without degrading training performance [1]}. Smart contracts are useful to build secure communication between the central authority and the distributed users for safe model updating [2]}.

blackFor privacy protection, differential privacy techniques can be employed in FLchain so that model computation is considered to be differentially private if the output is independent to particular data points from input data [2]}, [3]}. In this way, differential privacy is applied to locally generated models with artificial noise to reduce the possibility of individual record identification.
In the future, deploying more secure spatial decompositions algorithms is desired. For example, a solution is proposed in [8]} for indefeasible Laplace noise provision to address the problem of symmetric distribution of Laplace mechanisms in differential privacy. In this way, indefeasible noises are added to the randomly selected leaf child of each intermediate node of the private tree through multiplying Laplace noises, which helps prevent the possibility of noise cancellation and satisfy \(\epsilon \) -differential privacy.

Moreover, attack models should be considered for both blockchain mining and local training in FLchain. For example, greedy miners may exploit the FLchain system by augmenting their mining power to make control on the miner group to modify data blocks, or double-spending issues with a Sybil attack can make mining inefficient [9]}. blackMoreover, to enhance privacy in FLchain, some techniques such as homomorphic encryption [10]} would be very useful for privacy-preserving outsourced storage and computation in FL training where data can be encrypted before sharing on the blockchain for FL model aggregation. This is extremely important in FLchain applications such as healthcare [11]} in which health data and personal information are highly sensitive and privacy protection is highly needed.

